Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 141–150 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detection	Dec 23, 2024	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	2	5
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies	Mar 25, 2023	Anomaly DetectionComputational Efficiency	CodeCode Available	2	5
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching	Oct 16, 2024	Anomaly DetectionTime Series	CodeCode Available	2	5
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering	May 2, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2	5
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models	May 5, 2025	Anomaly ClassificationAnomaly Detection	CodeCode Available	2	5
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization	Jan 17, 2025	Anomaly DetectionImage-text matching	CodeCode Available	2	5
A Generalizable Anomaly Detection Method in Dynamic Graphs	Dec 21, 2024	Anomaly DetectionDiversity	CodeCode Available	2	5
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM	Mar 6, 2025	Anomaly DetectionLanguage Modeling	CodeCode Available	2	5
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features	Jul 17, 2024	Anomaly DetectionSelf-Driving Cars	CodeCode Available	2	5
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective	Sep 16, 2022	Anomaly DetectionAutoML	CodeCode Available	2	5

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified